code: def _create_gaussian_mixture(
      self,
      continuous_distribution: HyperparameterDistribution,
      trial_hyperparameters: List[HPSampledValue],
  ) -> DistributionMixture:
      use_logs = False
      if isinstance(continuous_distribution, LogSpaceDistributionMixin):
          use_logs = True
  
      use_quantized_distributions = False
      if isinstance(continuous_distribution, Quantized):
          use_quantized_distributions = True
          if isinstance(
              continuous_distribution.hd, LogSpaceDistributionMixin
          ):
              use_logs = True
  
      # Find means, std, amplitudes, min and max.
      (
          distribution_amplitudes,
          means,
          stds,
          distributions_mins,
          distributions_max,
      ) = self._adaptive_parzen_normal(
          continuous_distribution, trial_hyperparameters
      )
  
      # Create appropriate gaussian mixture that wrapped all hyperparams.
      gmm: DistributionMixture = (
          DistributionMixture.build_gaussian_mixture(
              distribution_amplitudes=distribution_amplitudes,
              means=means,
              stds=stds,
              distributions_mins=distributions_mins,
              distributions_max=distributions_max,
              use_logs=use_logs,
              use_quantized_distributions=use_quantized_distributions,
          )
      )
  
      return gmm
  
imports: from collections import Counter
  from operator import itemgetter
  from typing import Any, List, Tuple, Union
  import numpy
  from neuraxle.hyperparams.distributions import Choice, DiscreteHyperparameterDistribution, DistributionMixture, HPSampledValue, HyperparameterDistribution, LogSpaceDistributionMixin, PriorityChoice, Quantized
  from neuraxle.hyperparams.space import HyperparameterSamples, HyperparameterSpace
  from neuraxle.metaopt.data.reporting import RoundReport, TrialReport
  from neuraxle.metaopt.optimizer import BaseHyperparameterOptimizer, GridExplorationSampler
label_desc: Use image node_id 6 for calling the _DividedMixturesFactory obj's underlying member method code with example usage: obj._create_gaussian_mixture(continuous_distribution, trial_hyperparameters) and returns: gmm